<html><head><title>Aids Cases in England and Wales</title>
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<table width="100%"><tr><td>aids(gamlss.util)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
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<param name="keyword" value=" Aids Cases in England and Wales">
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<h2>Aids Cases in England and Wales</h2>


<h3>Description</h3>

<p>
The quarterly reported AIDS cases in the U.K. from
January 1983 to March 1994 obtained from the Public Health
Laboratory Service, Communicable Disease Surveillance Centre,
London.
</p>


<h3>Usage</h3>

<pre>data(aids)</pre>


<h3>Format</h3>

<p>
A data frame with 45 observations on the following 3 variables.
<dl>
<dt>y</dt><dd>the number of quarterly aids cases in England and Wales: a numeric vector</dd>
<dt>x</dt><dd>time in months from January 1983, 1:45 : a numeric vector</dd>
<dt>qrt</dt><dd>the quarterly seasonal effect a factor with 4 levels, [1=Q1 (Jan-March), 2=Q2 (Apr-June), 3=Q3 (July-Sept), 4=Q4 (Oct-Dec)]</dd>
</dl>

<h3>Details</h3>

<p>
The counts y can be modelled using  a (smooth) Poisson regression  model in time x with the quarterly effects i.e. 
cs(x,df=7)+qrt. Overdispersion persists,  so use a Negative Binomial distribution of type I or II.  
The data also can be used to find a break point in time, see Rigby and Stasinopoulos (1992).
</p>


<h3>Source</h3>

<p>
Public Health
Laboratory Service, Communicable Disease Surveillance Centre, London.
</p>


<h3>References</h3>

<p>
Stasinopoulos, D.M. and Rigby, R. A. (1992). Detecting break points in generalized linear models.
<EM>Computational Statistics and Data Analysis</EM>, <B>13</B>, 461&ndash;471.
</p>


<h3>Examples</h3>

<pre>
data(aids)
attach(aids)
plot(x,y,pch=21,bg=c("red","green3","blue","yellow")[unclass(qrt)])
detach(aids)
</pre>



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